我从https://towardsdatascience.com/how-to-build-from-scratch-a-content-based-movie-recommender-with-natural-language-processing-25ad400eb243开始关注一个内容库推荐系统。

计算出余弦相似度矩阵后，将创建一个函数，以推荐与我们输入的内容相似的前10个内容。

# creating a Series for the movie titles so they are associated to an ordered numerical
# list I will use in the function to match the indexes

indices = pd.Series(df.index)

#  defining the function that takes in movie title 
# as input and returns the top 10 recommended movies

def recommendations(title,cosine_sim = cosine_sim):

    # initializing the empty list of recommended movies
    recommended_movies = []

    # gettin the index of the movie that matches the title
    idx = indices[indices == title].index[0]

    # creating a Series with the similarity scores in descending order
    score_series = pd.Series(cosine_sim[idx]).sort_values(ascending = False)

    # getting the indexes of the 10 most similar movies
    top_10_indexes = list(score_series.iloc[1:11].index)

    # populating the list with the titles of the best 10 matching movies
    for i in top_10_indexes:
        recommended_movies.append(list(df.index)[i])

    return recommended_movies

以上内容为我输入的每个内容提供了前10个内容。我想创建一个数据框，其中第1列将是所有内容，第2-10列将是最相似的电影。因此，每一行都是原始内容，而排在前十位的是同类电影。我是python的新手，感谢您的帮助。